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    <name type="personal">
        <namePart type="given">Seung-won</namePart>
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    <name type="personal">
        <namePart type="given">Nan</namePart>
        <namePart type="family">Duan</namePart>
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    <name type="personal">
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    <abstract>We study compositional generalization, which aims to generalize on unseen combinations of seen structural elements, for code search. Unlike existing approaches of partially pursuing this goal, we study how to extract structural elements, which we name a template that directly targets compositional generalization. Thus we propose CTBERT, or Code Template BERT, representing codes using automatically extracted templates as building blocks. We empirically validate CTBERT on two public code search benchmarks, AdvTest and CSN. Further, we show that templates are complementary to data flow graphs in GraphCodeBERT, by enhancing structural context around variables.</abstract>
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%0 Conference Proceedings
%T Towards Compositional Generalization in Code Search
%A Han, Hojae
%A Hwang, Seung-won
%A Lu, Shuai
%A Duan, Nan
%A Choi, Seungtaek
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F han-etal-2022-towards
%X We study compositional generalization, which aims to generalize on unseen combinations of seen structural elements, for code search. Unlike existing approaches of partially pursuing this goal, we study how to extract structural elements, which we name a template that directly targets compositional generalization. Thus we propose CTBERT, or Code Template BERT, representing codes using automatically extracted templates as building blocks. We empirically validate CTBERT on two public code search benchmarks, AdvTest and CSN. Further, we show that templates are complementary to data flow graphs in GraphCodeBERT, by enhancing structural context around variables.
%R 10.18653/v1/2022.emnlp-main.737
%U https://aclanthology.org/2022.emnlp-main.737
%U https://doi.org/10.18653/v1/2022.emnlp-main.737
%P 10743-10750

Markdown (Informal)

[Towards Compositional Generalization in Code Search](https://aclanthology.org/2022.emnlp-main.737) (Han et al., EMNLP 2022)

• Towards Compositional Generalization in Code Search (Han et al., EMNLP 2022)

ACL

• Hojae Han, Seung-won Hwang, Shuai Lu, Nan Duan, and Seungtaek Choi. 2022. Towards Compositional Generalization in Code Search. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10743–10750, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.